More than ever before, it's become mission critical for marketers to break through the noise and reach their best customers. But can they actually reach them?
Data clean rooms offer a way forward for marketers to understand who they want to reach now, who they want to reach next and also who to avoid. When powered by AI, clean rooms can achieve this at scale. But not all clean rooms are ready for the AI revolution.
For sophisticated AI to achieve maximum results, brands need a data clean room equipped with pre-loaded data and identity.
A data clean room is a safe, pseudonymized space known for prospective customer data. This allows marketers to analyze marketing and advertising data from many different sources in one singular view, all in a way that protects the privacy of the data from each individual source.
Data clean rooms are not new, but they are becoming increasingly popular as data becomes more complex to consolidate across platforms and manage across geographies.
There are two general ways a data clean room can engage with AI—both of which are integral to the other.
Many brands use data clean room providers for data collaboration. This is possible because of the stringent privacy controls built into the tech designed to protect all parties collaborating inside a data clean room. This tech allows brands to work with various data sets, including those from trusted partners, to augment and enrich data. This fills in data gaps on known and prospective customers, creating a richer understanding and analysis of each person.
Most data clean rooms come as an empty container that brands must fill. Brands without a lot of first-party data may find it hard to draw value from clean room because they only have so much data to input for analysis. And, without an identity resolution solution, the data a brand does have may be incomplete, inaccurate and duplicative.
For AI to work effectively (and accurately) it requires quality data. And it needs a lot of it. Without a wide breadth and depth of accurate data, AI can't deliver meaningful insights.
A data clean room equipped with identity resolution can help prime a brand's data for AI engagement. Harmonized, cleansed, enhanced and connected data points provide a quality, accurate data foundation. A single identity spine creates stronger models for who their prospective customers might be.
Data clean rooms are far more than just a data repository, though. Next-gen data clean rooms do something with all the data and insights it produces.
This tech allows brands to work with various data sets, including those from trusted partners, to augment and enrich data. This fills in data gaps on known and prospective customers, creating a richer understanding and analysis of each person.
Using those insights, marketers can make smarter decisions: Who can I reach? Who should I reach? And who should I ignore all together?
Data clean rooms equipped with identity resolution enable a closed-loop, person-based marketing system. In short, they can:
Now imagine that process powered by AI.
Predictive AI powers a data clean room to use solid data to develop, activate, measure and learn in real-time and at scale.
AI and AI implementation is only going to accelerate. According to Epsilon research, 94% of marketers surveyed said they have already adopted AI for marketing, with 23% saying they're allocating 21-30% of their marketing budgets for AI. A 2025 Gallup poll shows that the percentage of U.S. employees who say they have used AI in their role as few times a year or more doubled in the past two years (from 21% to 40%.)
AI itself is also getting increasingly more complex. AI capabilities and accessibility are an ever-expanding landscape. Technology like agentic AI that anticipates and makes decisions autonomously across data, systems and people, will require data refinement. Custom models that require proprietary data will become essential.
Companies will need to invest in data quality assurance with incredibly high standards of reliability, accuracy and scalability. But even as the AI revolution ramps up, nearly half of marketers say they're worried about data quality. A recent Epsilon survey shows 49% of respondents said they’re concerned that model accuracy is affecting efficacy.
Investing in the right data clean room now will ensure brands are ready to take on new forms of AI as they arrive instead of chase after the bandwagon in the moment.
In a recent Digiday webinar sponsored by Epsilon, guest speaker Stephanie Liu, Senior Analyst, Forrester said data preparation is a critical step for brands looking to harness the power of AI. Without quality, accurate data, AI can't produce meaningful results. And as AI becomes more complex, quality control will become even more critical.
"AI is going to exacerbate those data issues," Liu said. "Just because the data is there doesn't mean it’s good data or the right data."
Epsilon Clean Room comes preloaded with data and identity, giving brands a foundational identity spine to bring first-party data together. We also offer proprietary audience data, giving brands a deeper view of their current customers.
But we go beyond simply having powerful tech. We offer pre-built predictive models and audiences for marketers to use, and access to audience strategists who can help with audience-first approaches and data strategies.
Learn more about Epsilon's Clean Room solution, how it works and what it can do for your business.
